Environmentally Extended Input-Output(EEIO)tables have become a powerful element in supporting information-based environmental and economic policies.National-and provincial-level 10 tables are currently published by t...Environmentally Extended Input-Output(EEIO)tables have become a powerful element in supporting information-based environmental and economic policies.National-and provincial-level 10 tables are currently published by the National Bureau of Statistics of the People's Republic of China according to well-defined conventions.However,county-level 10tables are not provided as a rule by official statistics organizations.This paper conducts an overview of compiling EEIO tables for environmental and resources accounting at the county level and then answers several questions:First,what kind of data should be prepared for the compilation of county-level EEIO tables?Second,how can we set up comprehensive EEIO tables at the county level?Third,regarding the survey methods and the indirect modeling,which one should be chosen to build EEIO tables at the county level?Finally,what policy questions could such a table answer?EEIO tables at the county level can be used to predict the economic impacts of environmental policies and to perform trend and scenario analysis.展开更多
Given the statistical gaps in material flow among provinces in China, a method was introduced to estimate regional physical imports and exports （RPIE）, which includes international and interregional imports/ exports...Given the statistical gaps in material flow among provinces in China, a method was introduced to estimate regional physical imports and exports （RPIE）, which includes international and interregional imports/ exports. This method uses provincial monetary input- output tables （MIOT） and international trade statistics. A coefficient matrix representing correlations between monetary value and physical mass for years 2000-2009 was obtained based on a detailed commodity classification and 22 material production sectors in MIOT. With the coefficient matrix as reference, RPIE was measured. Pilot calculation of both regional physical trade balance and domestic material consumption, as well as a brief analysis of these methods, were conducted using 2002 data.展开更多
The evolving dynamics of industrial convergence among the member countries of the Regional Comprehensive Economic Partnership(RCEP)framework have emerged as a significant subject that merits in-depth consideration and...The evolving dynamics of industrial convergence among the member countries of the Regional Comprehensive Economic Partnership(RCEP)framework have emerged as a significant subject that merits in-depth consideration and analysis.This study initially employs multi-regional input-output(MRIO)data and the social network analysis(SNA)method to delineate the levels and variation trends of this industrial convergence across the RCEP member countries.It then delves into the positive effects of this convergence phenomenon on the trade and investment fields of the member countries.The research findings indicate:(a)In 2006 and 2015,before the implementation of the RCEP,the RCEP member countries displayed a relatively close industrial convergence.The convergence levels exhibited a general upward trend on both the supply and the demand sides,but there were significant disparities in the levels of industrial convergence among the member countries.Furthermore,while the convergence in the three economic sectors showed an increasing trend,the development was uneven across the board.(b)Since the implementation of the RCEP,the trade ties among the member countries within the region have strengthened significantly,and the interplay between the countries’industrial and supply chains has been characterized by high-quality collaboration and demonstrated remarkable resilience.In addition,the convergence in the investment fields of the RCEP member countries and their respective industries has unleashed a wave of positive synergies.These findings offer valuable insights that can serve as a robust foundation for formulating effective policies to advance the growth and prosperity of the RCEP region.展开更多
Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such atta...Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.展开更多
基金supported by the Key Project of the Chinese Academy of Sciences[grant number KZZD-EW-08]the Exploratory Forefront Project for the Strategic Science Plan in IGSNRR,CAS
文摘Environmentally Extended Input-Output(EEIO)tables have become a powerful element in supporting information-based environmental and economic policies.National-and provincial-level 10 tables are currently published by the National Bureau of Statistics of the People's Republic of China according to well-defined conventions.However,county-level 10tables are not provided as a rule by official statistics organizations.This paper conducts an overview of compiling EEIO tables for environmental and resources accounting at the county level and then answers several questions:First,what kind of data should be prepared for the compilation of county-level EEIO tables?Second,how can we set up comprehensive EEIO tables at the county level?Third,regarding the survey methods and the indirect modeling,which one should be chosen to build EEIO tables at the county level?Finally,what policy questions could such a table answer?EEIO tables at the county level can be used to predict the economic impacts of environmental policies and to perform trend and scenario analysis.
文摘Given the statistical gaps in material flow among provinces in China, a method was introduced to estimate regional physical imports and exports （RPIE）, which includes international and interregional imports/ exports. This method uses provincial monetary input- output tables （MIOT） and international trade statistics. A coefficient matrix representing correlations between monetary value and physical mass for years 2000-2009 was obtained based on a detailed commodity classification and 22 material production sectors in MIOT. With the coefficient matrix as reference, RPIE was measured. Pilot calculation of both regional physical trade balance and domestic material consumption, as well as a brief analysis of these methods, were conducted using 2002 data.
基金This paper is a phased achievement of the humanities and social sciences project of the Chongqing Municipal Education Commission entitled“Research on the Integrated Development of the Digital Economy and Manufacturing Industry in Chongqing under the Development Paradigm of Dual Circulation”(Project No.:21SKGH229).
文摘The evolving dynamics of industrial convergence among the member countries of the Regional Comprehensive Economic Partnership(RCEP)framework have emerged as a significant subject that merits in-depth consideration and analysis.This study initially employs multi-regional input-output(MRIO)data and the social network analysis(SNA)method to delineate the levels and variation trends of this industrial convergence across the RCEP member countries.It then delves into the positive effects of this convergence phenomenon on the trade and investment fields of the member countries.The research findings indicate:(a)In 2006 and 2015,before the implementation of the RCEP,the RCEP member countries displayed a relatively close industrial convergence.The convergence levels exhibited a general upward trend on both the supply and the demand sides,but there were significant disparities in the levels of industrial convergence among the member countries.Furthermore,while the convergence in the three economic sectors showed an increasing trend,the development was uneven across the board.(b)Since the implementation of the RCEP,the trade ties among the member countries within the region have strengthened significantly,and the interplay between the countries’industrial and supply chains has been characterized by high-quality collaboration and demonstrated remarkable resilience.In addition,the convergence in the investment fields of the RCEP member countries and their respective industries has unleashed a wave of positive synergies.These findings offer valuable insights that can serve as a robust foundation for formulating effective policies to advance the growth and prosperity of the RCEP region.
文摘Detecting sophisticated cyberattacks,mainly Distributed Denial of Service(DDoS)attacks,with unexpected patterns remains challenging in modern networks.Traditional detection systems often struggle to mitigate such attacks in conventional and software-defined networking(SDN)environments.While Machine Learning(ML)models can distinguish between benign and malicious traffic,their limited feature scope hinders the detection of new zero-day or low-rate DDoS attacks requiring frequent retraining.In this paper,we propose a novel DDoS detection framework that combines Machine Learning(ML)and Ensemble Learning(EL)techniques to improve DDoS attack detection and mitigation in SDN environments.Our model leverages the“DDoS SDN”dataset for training and evaluation and employs a dynamic feature selection mechanism that enhances detection accuracy by focusing on the most relevant features.This adaptive approach addresses the limitations of conventional ML models and provides more accurate detection of various DDoS attack scenarios.Our proposed ensemble model introduces an additional layer of detection,increasing reliability through the innovative application of ensemble techniques.The proposed solution significantly enhances the model’s ability to identify and respond to dynamic threats in SDNs.It provides a strong foundation for proactive DDoS detection and mitigation,enhancing network defenses against evolving threats.Our comprehensive runtime analysis of Simultaneous Multi-Threading(SMT)on identical configurations shows superior accuracy and efficiency,with significantly reduced computational time,making it ideal for real-time DDoS detection in dynamic,rapidly changing SDNs.Experimental results demonstrate that our model achieves outstanding performance,outperforming traditional algorithms with 99%accuracy using Random Forest(RF)and K-Nearest Neighbors(KNN)and 98%accuracy using XGBoost.
